with Categorical Features - Lightgbm Typing CST Test

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with Categorical Features — Lightgbm Code

Classification using LightGBM with categorical features.

import lightgbm as lgb
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Sample data
data = pd.DataFrame({'feature1':[1,2,3,4,5], 'feature2':['A','B','A','B','C'], 'label':[0,1,0,1,0]})
data['feature2'] = data['feature2'].astype('category')
X = data[['feature1','feature2']]
y = data['label']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

train_data = lgb.Dataset(X_train, label=y_train, categorical_feature=['feature2'])
params = {'objective':'binary','metric':'binary_logloss'}
model = lgb.train(params, train_data, num_boost_round=50)
y_pred = model.predict(X_test)
y_pred_labels = (y_pred > 0.5).astype(int)
print('Accuracy:', accuracy_score(y_test, y_pred_labels))

Lightgbm Language Guide

LightGBM (Light Gradient Boosting Machine) is a fast, distributed, high-performance gradient boosting framework based on decision tree algorithms, used for ranking, classification, and many other machine learning tasks.

Primary Use Cases

▸Binary and multiclass classification
▸Regression problems
▸Ranking tasks (learning-to-rank)
▸Feature selection and importance analysis
▸Integration in ML pipelines for large-scale structured data

Notable Features

▸Faster training with histogram-based decision tree algorithm
▸Low memory usage compared to XGBoost
▸Supports parallel and GPU learning
▸Handles categorical features directly
▸Scales efficiently with large datasets

Origin & Creator

LightGBM was developed by Microsoft’s DMTK team and released in 2016 to provide a faster and more memory-efficient gradient boosting framework compared to existing solutions.

Industrial Note

LightGBM is widely used in Kaggle competitions, finance, advertising, recommendation systems, and any scenario requiring high-speed gradient boosting on large datasets.

More Lightgbm Typing Exercises

LightGBM Simple Classification Example LightGBM Binary Classification Example LightGBM Regression Example LightGBM Early Stopping Example LightGBM Feature Importance Example LightGBM Cross Validation Example LightGBM Regression with Validation LightGBM Multi-class Classification Example

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with Categorical Features — Lightgbm Code

Classification using LightGBM with categorical features.

import lightgbm as lgb import pandas as pd from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # Sample data data = pd.DataFrame({'feature1':[1,2,3,4,5], 'feature2':['A','B','A','B','C'], 'label':[0,1,0,1,0]}) data['feature2'] = data['feature2'].astype('category') X = data[['feature1','feature2']] y = data['label'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) train_data = lgb.Dataset(X_train, label=y_train, categorical_feature=['feature2']) params = {'objective':'binary','metric':'binary_logloss'} model = lgb.train(params, train_data, num_boost_round=50) y_pred = model.predict(X_test) y_pred_labels = (y_pred > 0.5).astype(int) print('Accuracy:', accuracy_score(y_test, y_pred_labels))

Lightgbm Language Guide

Primary Use Cases

▸Binary and multiclass classification
▸Regression problems
▸Ranking tasks (learning-to-rank)
▸Feature selection and importance analysis
▸Integration in ML pipelines for large-scale structured data

Notable Features

▸Faster training with histogram-based decision tree algorithm
▸Low memory usage compared to XGBoost
▸Supports parallel and GPU learning
▸Handles categorical features directly
▸Scales efficiently with large datasets

Origin & Creator

LightGBM was developed by Microsoft’s DMTK team and released in 2016 to provide a faster and more memory-efficient gradient boosting framework compared to existing solutions.

Industrial Note

LightGBM is widely used in Kaggle competitions, finance, advertising, recommendation systems, and any scenario requiring high-speed gradient boosting on large datasets.